Content of TOPICAL REVIEW—Post-Moore era: Materials and device physics in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For selected: Download citations EndNote Ris BibTeX Toggle thumbnails Select Advances in neuromorphic computing: Expanding horizons for AI development through novel artificial neurons and in-sensor computing Yubo Yang(杨玉波), Jizhe Zhao(赵吉哲), Yinjie Liu(刘胤洁), Xiayang Hua(华夏扬), Tianrui Wang(王天睿), Jiyuan Zheng(郑纪元), Zhibiao Hao(郝智彪), Bing Xiong(熊兵), Changzheng Sun(孙长征), Yanjun Han(韩彦军), Jian Wang(王健), Hongtao Li(李洪涛), Lai Wang(汪莱), and Yi Luo(罗毅) Chin. Phys. B, 2024, 33 (3): 030702.   DOI: 10.1088/1674-1056/ad1c58 Abstract （475）   HTML （17）    PDF （10310KB）（314）       Knowledge map    AI development has brought great success to upgrading the information age. At the same time, the large-scale artificial neural network for building AI systems is thirsty for computing power, which is barely satisfied by the conventional computing hardware. In the post-Moore era, the increase in computing power brought about by the size reduction of CMOS in very large-scale integrated circuits (VLSIC) is challenging to meet the growing demand for AI computing power. To address the issue, technical approaches like neuromorphic computing attract great attention because of their feature of breaking Von-Neumann architecture, and dealing with AI algorithms much more parallelly and energy efficiently. Inspired by the human neural network architecture, neuromorphic computing hardware is brought to life based on novel artificial neurons constructed by new materials or devices. Although it is relatively difficult to deploy a training process in the neuromorphic architecture like spiking neural network (SNN), the development in this field has incubated promising technologies like in-sensor computing, which brings new opportunities for multidisciplinary research, including the field of optoelectronic materials and devices, artificial neural networks, and microelectronics integration technology. The vision chips based on the architectures could reduce unnecessary data transfer and realize fast and energy-efficient visual cognitive processing. This paper reviews firstly the architectures and algorithms of SNN, and artificial neuron devices supporting neuromorphic computing, then the recent progress of in-sensor computing vision chips, which all will promote the development of AI. Select Silicon-based optoelectronic heterogeneous integration for optical interconnection Le-Liang Li(李乐良), Gui-Ke Li(李贵柯), Zhao Zhang(张钊), Jian Liu(刘剑), Nan-Jian Wu(吴南健), Kai-You Wang(王开友), Nan Qi(祁楠), and Li-Yuan Liu(刘力源) Chin. Phys. B, 2024, 33 (2): 024201.   DOI: 10.1088/1674-1056/ad0e5b Abstract （461）   HTML （8）    PDF （3660KB）（734）       Knowledge map    The performance of optical interconnection has improved dramatically in recent years. Silicon-based optoelectronic heterogeneous integration is the key enabler to achieve high performance optical interconnection, which not only provides the optical gain which is absent from native Si substrates and enables complete photonic functionalities on chip, but also improves the system performance through advanced heterogeneous integrated packaging. This paper reviews recent progress of silicon-based optoelectronic heterogeneous integration in high performance optical interconnection. The research status, development trend and application of ultra-low loss optical waveguides, high-speed detectors, high-speed modulators, lasers and 2D, 2.5D, 3D and monolithic integration are focused on. Select The rise of supercapacitor diodes: Current progresses and future challenges Hongyun Ma(马鸿云), Lingxiao Ma(马凌霄), Huasheng Bi(毕华盛), and Wei Lan(兰伟) Chin. Phys. B, 2024, 33 (2): 028201.   DOI: 10.1088/1674-1056/ad1171 Abstract （324）   HTML （4）    PDF （5566KB）（252）       Knowledge map    Supercapacitor has been widely known as a representative electrochemical energy storage device with high power density and long lifespan. Recently, with the deeper understanding of its charge storage mechanism, unidirectional-charging supercapacitor, also called supercapacitor diode (CAPode), is successfully developed based on the ion-sieving effect of its working electrode towards electrolyte ions. Because CAPode integrates mobile ion and mobile electron in one hybrid circuit, it has a great potential in the emerging fields of ion/electron coupling logic operations, human-machine interface, neural network interaction, and in vivo diagnosis and treatment. Accordingly, we herein elucidate the working mechanism and design philosophy of CAPode, and summarize the electrode materials that are suitable for constructing CAPode. Meanwhile, some other supercapacitor-based devices beyond CAPode are also introduced, and their potential applications are instructively presented. Finally, we outline the challenges and chances of CAPode-related techniques.